(working title)
Esben Lykke, PhD student
28 januar, 2023
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Overview Network plot of data preparation steps
network plot
Basic Features
ACC derived features1
Sensor-Independent Features2
Forger, Jewett, and Kronauer (1999): a so-called cubic van der Pol equation
\[\frac{dx_c}{dt}=\frac{\pi}{12}\begin{cases}\mu(x_c-\frac{4x^3}{3})-x\begin{bmatrix}(\frac{24}{0.99669\tau_x})^2+kB\end{bmatrix}\end{cases}\]
This thing is dependent on ambient light and body temperature!
Walch et al. (2019) incorporated this feature using step counts from the Apple Watch
But as demonstrated by Walch et al. (2019), a simple cosine function does the tricks just as well :)
| Performance Metrics | ||||
| Grouped by Event Prediction | ||||
| Logistic Regression | Neural Network | Decision Tree | XGboost | |
|---|---|---|---|---|
| In-bed Prediction | ||||
| F1 Score | 94.26% | 95.88% | 95.33% | 95.79% |
| Accuracy | 92.95% | 95.10% | 94.52% | 95.01% |
| Sensitivity | 97.96% | 96.40% | 94.68% | 96.02% |
| Specificity | 85.71% | 93.24% | 94.29% | 93.56% |
| Sleep Prediction | ||||
| F1 Score | 93.08% | 94.35% | 93.90% | 94.29% |
| Accuracy | 90.87% | 92.77% | 92.29% | 92.71% |
| Sensitivity | 94.19% | 92.59% | 90.98% | 92.20% |
| Specificity | 84.65% | 93.09% | 94.73% | 93.69% |
Performance of the models to predict each class seperately, i.e., “sleep” and “in-bed”.
| Performance Metrics | ||||
| Grouped by Event Prediction | ||||
| Logistic Regression | Neural Network | Decision Tree | XGboost | |
|---|---|---|---|---|
| In-Bed Awake | ||||
| F1 Score | 96.00% | 96.38% | 96.50% | 96.54% |
| Accuracy | 92.38% | 93.09% | 93.32% | 93.39% |
| Sensitivity | 97.56% | 97.90% | 98.17% | 98.16% |
| Specificity | 13.11% | 19.66% | 19.24% | 20.51% |
| In-Bed Sleep | ||||
| F1 Score | 93.12% | 94.33% | 93.90% | 94.29% |
| Accuracy | 90.91% | 92.74% | 92.29% | 92.71% |
| Sensitivity | 94.25% | 92.65% | 90.98% | 92.22% |
| Specificity | 84.65% | 92.90% | 94.73% | 93.65% |
Performance of the models to predict each combined class, i.e., “sleep” + “in-bed”.
https://github.com/esbenlykke/sleep_study